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 单点检测 [dān diǎn jiǎn cè添加此单词到默认生词本
one point sensing

  1. 针对目前国内外大中城市中普遍存在的无检测器信号交叉口车道交通流信息难于获取的情况,基于信号控制交叉口车道之间的相关性,综合应用聚类分析和逐步回归法预测单点检测器信号控制交叉口车道流量.首先应用聚类分析将单点检测器信号控制交叉口的车道与有检测器信号控制交叉口的车道交通流量进行聚类,然后在聚类分析结果的基础上随机选取车道交通流量样本运用逐步回归法预测单点检测器信号控制交叉口的车道流量,此方法经过南京市的具体车道流量数据验证.此类问题的解决,可广泛应用于城市交通流诱导系统以及交通控制系统.
    Because of the difficulty to obtain the traffic flow information of lanes at non-detector intersections in most metropolises of the world,based on the relationships between the lanes of signal-controlled intersections,cluster analysis and stepwise regression are integrated to predict the traffic volume of lanes at non-detector isolated controlled intersections.First cluster analysis is used to cluster the lanes of non-detector isolated signal-controlled intersections and the lanes of all signal-controlled intersections with detectors.Then, by the results of cluster analysis,the traffic volume samples are selected randomly and stepwise regression is used to predict the traffic volume of lanes at non-detector isolated signal-controlled intersections.The method is tested by the traffic volume data of lanes of the road network of Nanjing city.The problem of predicting the traffic volume of lanes at non-detector isolated signal-controlled intersections was resolved and can be widely used in urban traffic flow guidance and urban traffic control in cities without enough intersections equipped with detectors.
  2. 在入侵检测中使用个的支持向量机容易因"单点失效"而危害系统安全.提出一种支持向量机集成的方法来进行入侵检测.它采用负相关学习技术,在误差项中使用相关性惩罚因子使得生成的分类器有更好的多样性和精度;算法采用进化策略来自动地确定个体支持向量机的超参数,避免了需要了解问题的先验知识;最后,采用集成技术来组合个体支持向量机的检测结果.仿真实验表明这一方法有更好的检测性能,并且这种分布式并行检测方法有利于增加入侵检测系统的鲁棒性.
    The individual SVM is prone to fail in the intrusion detection for the fragility of being attacked.This paper addresses a method using a support vector machines ensemble approach based on negative correlation learning for intrusion detection.Using a correlation penalty term in the error function,the aggregate members can be accurate and diverse.And the evolutionary strategy is considered as the best way to automatically determine the individ.ual SVMs hyperparameters.At last we combine the results of all individual SVMs using ensemble technique.This distributed parallel detection can strengthen the robustness of the system.Simulation results show the effectiveness of the method presented in this paper.
  3. 在入侵检测中使用个的支持向量机容易因"单点失效"而危害系统安全.提出一种基于支持向量机集成的方法来进行入侵检测.它采用负相关学习技术,在误差项中使用相关性惩罚因子使得生成的分类器有更好的多样性和精度;算法采用进化策略来自动地确定个体支持向量机的超参数,避免了需要了解问题的先验知识;最后,采用集成技术来组合个体支持向量机的检测结果.仿真实验表明这一方法有更好的检测性能,并且这种分布式并行检测方法有利于增加入侵检测系统的鲁棒性.
    The individual SVM is prone to fail in the intrusion detection for the fragility of being attacked.This paper addresses a method using a support vector machines ensemble approach based on negative correlation learning for intrusion detection.Using a correlation penalty term in the error function,the aggregate members can be accurate and diverse.And the evolutionary strategy is considered as the best way to automatically determine the individ.ual SVMs hyperparameters.At last we combine the results of all individual SVMs using ensemble technique.This distributed parallel detection can strengthen the robustness of the system.Simulation results show the effectiveness of the method presented in this paper.



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